The present invention relates to check stock verification systems, and is particularly directed to methods of processing a check in a check stock verification system.
A known system for detecting fraudulent checks is a check stock verification system in which a presented check is compared with a “reference” check which is associated with the checking account of the presented check. The comparison is automated in that there is no human intervention. Typically, a number of pre-printed elements from the presented check are compared with known pre-printed elements from the reference check to ensure that the presented check has not been forged or altered. If the pre-printed elements match, then payment of the presented check amount is approved. However, if the pre-printed elements do not match, then payment of the presented check amount is not approved and a human operator is alerted of a potentially fraudulent check.
From time to time, a presented check is authentic, but for some reason the pre-printed elements on the presented check do not match the pre-printed elements on the reference check. The mismatch could occur for any number of different reasons. As an example, the mismatch could occur because of the pre-printed elements on the presented check having been worn away. As another example, the manner in which the presented check was scanned may be different from the manner in which the reference check was scanned, resulting in different image resolutions, orientations, and the like. As still another example, the quality of the pre-printed elements on the presented check may be relatively poor as compared to the quality of the pre-printed elements on the presented check. This could occur if, for example, the image of the reference check was previously migrated from an older system.
A determination of a mismatch when the presented check is in fact authentic is known as a “false positive”. The total number of false positives in a single day may be large. As an example, the rate of false positives may be ten percent, and the total number of presented checks being processed by the check stock verification system may be, for example, over 100,000 checks. If 100,000 presented checks are processed and the rate of false positives is ten percent, then there would be approximately 10,000 checks in a single day for manual review.
Since the number of false positives presented to a human operator for manual review would be relatively large, an unfavorable business case may arise where the cost to review exceeds the cost of the fraud losses avoided. Or equally problematic, the manual review process may be unsuccessful because of the “needle in the haystack” syndrome in which a human operator may not be alert enough to identify and sort out the relatively few presented checks from a group of thousands of checks presented for manual review. It would be desirable to reduce the number of false positives presented to a human operator for manual review so that the human operator can focus on fewer presented checks and, therefore, perform the job more quickly and with greater accuracy.